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Optimization of power grid material warehousing and supply chain distribution path planning based on improved PSO
Junping Ge1, Tao Wang2, Kairui Hu3
1Jinhua Power Supply Company, State Grid Zhejiang Electric Power Co., Ltd., Jinhua, 321000, China. ejhgjp@126.com.
Scientific Reports
|December 22, 2025
Summary
This study introduces an advanced optimization model for smart grid emergency logistics, improving material classification accuracy and significantly reducing delivery times during disasters. The model enhances efficiency and adaptability in complex supply chain scenarios.
Area of Science:
- Engineering
- Computer Science
- Operations Research
Background:
- Smart grid construction increases complexity in power grid material warehousing and emergency distribution.
- Traditional methods struggle with search efficiency and adaptability in dynamic disaster scenarios.
Purpose of the Study:
- To develop an efficient and scalable optimization model for smart grid material management and emergency distribution.
- To address limitations in traditional methods concerning search efficiency and dynamic scenario adaptability.
Main Methods:
- Integration of a multi-strategy collaborative adaptive chaotic particle swarm optimization algorithm and an improved imperialist competitive algorithm.
- Incorporation of multi-strategy chaotic disturbances and adaptive inertia weights for inventory parameter optimization.
- Implementation of fishbone warehouse layout, immune penalty correction, and pheromone adaptive balancing for enhanced picking and path planning.
Main Results:
- Achieved 98.73% accuracy in material classification.
- Reduced delivery time from 6.78 h to 4.56 h in earthquake scenarios.
- Demonstrated single iteration calculation time of 0.45s for supply chain distribution path solutions, combining stability and efficiency.
Conclusions:
- The proposed model offers significant advantages in multi-objective optimization, warehouse layout planning, and emergency logistics path scheduling.
- Provides feasible technical solutions for smart grid material management and emergency distribution.
- Offers new methodological references for complex supply chain optimization research.
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